Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.

Coronary computed tomographic angiography: A review of the techniques, protocols, pitfalls, and radiation dose

Coronary computed tomographic angiography (CCTA) is a viable alternative to catheter coronary angiography for several clinical indications, chiefly because it is fast and non-invasive. For effective clinical use of CCTA, various technical and patient factors should be considered. In this brief review article, we discuss the indication and contraindications for CCTA, technical requirements for CCTA including radiation dose, patient preparation principles, image post-processing, and pitfalls and artifacts of CCTA.

The Latest Progress and Development Trend in the Research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the Field of Health Care

The current status of the research of Ballistocardiography (BCG) and Seismocardiogram (SCG) in the field of medical treatment, health care and nursing was analyzed systematically, and the important direction in the research was explored, to provide reference for the relevant researches. This study, based on two large databases, CNKI and PubMed, used the bibliometric analysis method to review the existing documents in the past 20 years, and made analyses on the literature of BCG and SCG for their annual changes, main countries/regions, types of research, frequently-used subject words, and important research subjects. The results show that the developed countries have taken a leading position in the researches in this field, and have made breakthroughs in some subjects, but their research results have been mainly gained in the area of research and development of the technologies, and very few have been actually industrialized into commodities. This means that in the future the researchers should focus on the transformation of BCG and SCG technologies into commercialized products, and set up quantitative health assessment models, so as to become the daily tools for people to monitor their health status and manage their own health, and as the main approaches of improving the quality of life and preventing diseases for individuals.

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

The Delineation of Fiducial Points for Non-Contact Radar Seismocardiogram Signals Without Concurrent ECG

OBJECTIVE

Non-contact sensing of seismocardiogram (SCG) signals through a microwave Doppler radar is promising for biomedical applications. However, the delineation of fiducial points for radar SCG still relies on concurrent ECG which requires a contact sensor and limits the complete non-contact detection of SCG.

METHODS

Instead of ECG, a new reference signal, the radar displacement signal of heartbeat (RDH), was derived through the complex Fourier transform and the band pass filtering of the radar signal. The RDH signal was used to locate each cardiac cycle and mask the systolic profile, which was further used to detect an important fiducial point, aortic valve opening (AO). The beat-to-beat interval was estimated from AO-AO interval and compared with the gold standard, ECG R-to-R interval.

RESULTS

For the 22 subjects in the study, the evaluation of the AOs detected by RDH (AO_RDH) shows the average detection ratio can reach 90%, indicating a high ratio of the AO_RDH that are exactly the same as AO detected using the ECG R-wave (AO_ECG). Additionally, the left ventricular ejection time (LVET) values estimated from the ensemble averaged radar waveform through AO_RDH segmentation are within 2 ms of those through AO_ECG segmentation, for all the detected subjects. Further analysis demonstrates that the beat-to-beat intervals calculated from AO_RDH have an average root-mean-square-deviation (RMSD) of 53.73 ms when compared with ECG R-to-R intervals, and have an average RMSD of 23.47 ms after removing the beats in which AO cannot be identified.

CONCLUSIONS

Radar signal RDH can be used as a reference signal to delineate fiducial points for non-contact radar SCG signals.

SIGNIFICANCE

This study can be applied to develop complete non-contact sensing of SCG and monitoring of vital signs, where contact-based SCG is not feasible.

Localizing Placement of Cardiomechanical Sensors during Dynamic Periods via Template Matching

Captured with a chest-mounted sensor, the seismo- cardiogram (SCG) is a useful signal for assessing cardiomechanical function. However, the reliability of information obtained from this signal often depends upon sensor location. This has important practical implications, as consistent placement is not guaranteed in at-home and other uncontrolled settings. Building on prior research that localized SCG sensor placement when the patient was at rest - which may not be the case in practical settings - this work presents a more robust method which is able to localize sensor placement during dynamic periods, specifically exercise recovery. This was accomplished via a template-based signal quality index (SQI), which was used to infer sensor location using a variety of classifiers. While prior work generated synthetic templates for this task using an averaging method, it is shown that selecting representative templates from the training set instead enables, for the first time, SCG sensor localization during dynamic periods without patient-specific calibration. With this method, a peak accuracy of 83.32% was achieved for correctly classifying sensor position among five tested positions, with avenues for improvement of these results also presented.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Recent Advances in Seismocardiography

Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

Near Real-Time Implementation of An Adaptive Seismocardiography – ECG Multimodal Framework for Cardiac Gating

Objective: Accurate gating for data acquisition of computed tomography (CT) is crucial to obtaining high quality images for diagnosing cardiovascular diseases. To illustrate the feasibility of an optimized cardiac gating strategy, we present a near real-time implementation based on fusing seismocardiography (SCG) and ECG. Methods: The implementation was achieved via integrating commercial hardware and software platforms. Testing was performed on five healthy subjects (age: 24–27; m/f: 4/1) and three cardiac patients (age: 41–71; m/f: 2/1), and compared with baseline quiescence derived from echocardiography. Results: The average latency introduced by computerized processing was 5.1 ms, well within a 100 ms tolerance bounded by data accumulation time for quiescence prediction. The average prediction error associated with conventional ECG-only versus SCG-ECG-based method over all subjects were 59.58 ms and 27.24 ms, respectively. Discussion: The results demonstrate that the multimodal framework can achieve improved quiescence prediction accuracy over the ECG-only-based method in near real-time.

An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks

To more accurately trigger data acquisition and reduce radiation exposure of coronary computed tomography angiography (CCTA), a multimodal framework utilizing both electrocardiography (ECG) and seismocardiography (SCG) for CCTA prospective gating is presented. Relying upon a three-layer artificial neural network that adaptively fuses individual ECG- and SCG-based quiescence predictions on a beat-by-beat basis, this framework yields a personalized quiescence prediction for each cardiac cycle. This framework was tested on seven healthy subjects (age: 22-48; m/f: 4/3) and eleven cardiac patients (age: 31-78; m/f: 6/5). Seventeen out of 18 benefited from the fusion-based prediction as compared to the ECG-only-based prediction, the traditional prospective gating method. Only one patient whose SCG was compromised by noise was more suitable for ECG-only-based prediction. On average, our fused ECG-SCG-based method improves cardiac quiescence prediction by 47% over ECG-only-based method; with both compared against the gold standard, B-mode echocardiography. Fusion-based prediction is also more resistant to heart rate variability than ECG-only- or SCG-only-based prediction. To assess the clinical value, the diagnostic quality of the CCTA reconstructed volumes from the quiescence derived from ECG-, SCG- and fusion-based predictions were graded by a board-certified radiologist using a Likert response format. Grading results indicated the fusion-based prediction improved diagnostic quality. ECG may be a sub-optimal modality for quiescence prediction and can be enhanced by the multimodal framework. The combination of ECG and SCG signals for quiescence prediction bears promise for a more personalized and reliable approach than ECG-only-based method to predict cardiac quiescence for prospective CCTA gating.

Hypersampling of pseudo-periodic signals by analytic phase projection

A method to upsample insufficiently sampled experimental time series of pseudo-periodic signals is proposed. The result is an estimate of the pseudo-periodic cycle underlying the signal. This "hypersampling" requires a sufficiently sampled reference signal that defines the pseudo-periodic dynamics. The time series and reference signal are combined by projecting the time series values to the analytic phase of the reference signal. The resulting estimate of the pseudo-periodic cycle has a considerably higher effective sampling rate than the time series. The procedure is applied to time series of MRI images of the human brain. As a result, the effective sampling rate could be increased by three orders of magnitude. This allows for capturing the waveforms of the very fast cerebral pulse waves traversing the brain. Hypersampling is numerically compared to the more commonly used retrospective gating. An outlook regarding EEG and optical recordings of brain activity as the reference signal is provided.

Artifact Noise Removal Techniques on Seismocardiogram Using Two Tri-Axial Accelerometers

The aim of this study is on the investigation of motion noise removal techniques using two-accelerometer sensor system and various placements of the sensors on gentle movement and walking of the patients. A Wi-Fi based data acquisition system and a framework on Matlab are developed to collect and process data while the subjects are in motion. The tests include eight volunteers who have no record of heart disease. The walking and running data on the subjects are analyzed to find the minimal-noise bandwidth of the SCG signal. This bandwidth is used to design filters in the motion noise removal techniques and peak signal detection. There are two main techniques of combining signals from the two sensors to mitigate the motion artifact: analog processing and digital processing. The analog processing comprises analog circuits performing adding or subtracting functions and bandpass filter to remove artifact noises before entering the data acquisition system. The digital processing processes all the data using combinations of total acceleration and z-axis only acceleration. The two techniques are tested on three placements of accelerometer sensors including horizontal, vertical, and diagonal on gentle motion and walking. In general, the total acceleration and z-axis acceleration are the best techniques to deal with gentle motion on all sensor placements which improve average systolic signal-noise-ratio (SNR) around 2 times and average diastolic SNR around 3 times comparing to traditional methods using only one accelerometer. With walking motion, ADDER and z-axis acceleration are the best techniques on all placements of the sensors on the body which enhance about 7 times of average systolic SNR and about 11 times of average diastolic SNR comparing to only one accelerometer method. Among the sensor placements, the performance of horizontal placement of the sensors is outstanding comparing with other positions on all motions.

A novel dual gating approach using joint inertial sensors: implications for cardiac PET imaging

Positron emission tomography (PET) is a non-invasive imaging technique which may be considered as the state of art for the examination of cardiac inflammation due to atherosclerosis. A fundamental limitation of PET is that cardiac and respiratory motions reduce the quality of the achieved images. Current approaches for motion compensation involve gating the PET data based on the timing of quiescent periods of cardiac and respiratory cycles. In this study, we present a novel gating method called microelectromechanical (MEMS) dual gating which relies on joint non-electrical sensors, i.e. tri-axial accelerometer and gyroscope. This approach can be used for optimized selection of quiescent phases of cardiac and respiratory cycles. Cardiomechanical activity according to echocardiography observations was investigated to confirm whether this dual sensor solution can provide accurate trigger timings for cardiac gating. Additionally, longitudinal chest motions originating from breathing were measured by accelerometric- and gyroscopic-derived respiratory (ADR and GDR) tracking. The ADR and GDR signals were evaluated against Varian real-time position management (RPM) signals in terms of amplitude and phase. Accordingly, high linear correlation and agreement were achieved between the reference electrocardiography, RPM, and measured MEMS signals. We also performed a Ge-68 phantom study to evaluate possible metal artifacts caused by the integrated read-out electronics including mechanical sensors and semiconductors. The reconstructed phantom images did not reveal any image artifacts. Thus, it was concluded that MEMS-driven dual gating can be used in PET studies without an effect on the quantitative or visual accuracy of the PET images. Finally, the applicability of MEMS dual gating for cardiac PET imaging was investigated with two atherosclerosis patients. Dual gated PET images were successfully reconstructed using only MEMS signals and both qualitative and quantitative assessments revealed encouraging results that warrant further investigation of this method.